126 research outputs found
The Revolution in Astronomy Education: Data Science for the Masses
As our capacity to study ever-expanding domains of our science has increased
(including the time domain, non-electromagnetic phenomena, magnetized plasmas,
and numerous sky surveys in multiple wavebands with broad spatial coverage and
unprecedented depths), so have the horizons of our understanding of the
Universe been similarly expanding. This expansion is coupled to the exponential
data deluge from multiple sky surveys, which have grown from gigabytes into
terabytes during the past decade, and will grow from terabytes into Petabytes
(even hundreds of Petabytes) in the next decade. With this increased vastness
of information, there is a growing gap between our awareness of that
information and our understanding of it. Training the next generation in the
fine art of deriving intelligent understanding from data is needed for the
success of sciences, communities, projects, agencies, businesses, and
economies. This is true for both specialists (scientists) and non-specialists
(everyone else: the public, educators and students, workforce). Specialists
must learn and apply new data science research techniques in order to advance
our understanding of the Universe. Non-specialists require information literacy
skills as productive members of the 21st century workforce, integrating
foundational skills for lifelong learning in a world increasingly dominated by
data. We address the impact of the emerging discipline of data science on
astronomy education within two contexts: formal education and lifelong
learners.Comment: 12 pages total: 1 cover page, 1 page of co-signers, plus 10 pages,
State of the Profession Position Paper submitted to the Astro2010 Decadal
Survey (March 2009
Galaxy Zoo: Disentangling the Environmental Dependence of Morphology and Colour
We analyze the environmental dependence of galaxy morphology and colour with
two-point clustering statistics, using data from the Galaxy Zoo, the largest
sample of visually classified morphologies yet compiled, extracted from the
Sloan Digital Sky Survey. We present two-point correlation functions of spiral
and early-type galaxies, and we quantify the correlation between morphology and
environment with marked correlation functions. These yield clear and precise
environmental trends across a wide range of scales, analogous to similar
measurements with galaxy colours, indicating that the Galaxy Zoo
classifications themselves are very precise. We measure morphology marked
correlation functions at fixed colour and find that they are relatively weak,
with the only residual correlation being that of red galaxies at small scales,
indicating a morphology gradient within haloes for red galaxies. At fixed
morphology, we find that the environmental dependence of colour remains strong,
and these correlations remain for fixed morphology \textit{and} luminosity. An
implication of this is that much of the morphology--density relation is due to
the relation between colour and density. Our results also have implications for
galaxy evolution: the morphological transformation of galaxies is usually
accompanied by a colour transformation, but not necessarily vice versa. A
spiral galaxy may move onto the red sequence of the colour-magnitude diagram
without quickly becoming an early-type. We analyze the significant population
of red spiral galaxies, and present evidence that they tend to be located in
moderately dense environments and are often satellite galaxies in the outskirts
of haloes. Finally, we combine our results to argue that central and satellite
galaxies tend to follow different evolutionary paths.Comment: 19 pages, 18 figures. Accepted for publication in MNRA
Galaxy Zoo Green Peas: discovery of a class of compact extremely star-forming galaxies
‘The definitive version is available at www3.interscience.wiley.com '. Copyright Royal Astronomical Society. DOI: 10.1111/j.1365-2966.2009.15383.xWe investigate a class of rapidly growing emission line galaxies, known as 'Green Peas', first noted by volunteers in the Galaxy Zoo project because of their peculiar bright green colour and small size, unresolved in Sloan Digital Sky Survey imaging. Their appearance is due to very strong optical emission lines, namely [O iii]λ5007 Å, with an unusually large equivalent width of up to ∼1000 Å. We discuss a well-defined sample of 251 colour-selected objects, most of which are strongly star forming, although there are some active galactic nuclei interlopers including eight newly discovered narrow-line Seyfert 1 galaxies. The star-forming Peas are low-mass galaxies (M∼ 108.5–1010 M⊙) with high star formation rates (∼10 M⊙ yr−1) , low metallicities (log[O/H]+ 12 ∼ 8.7) and low reddening [ E(B−V) ≤ 0.25 ] and they reside in low-density environments. They have some of the highest specific star formation rates (up to ∼10−8 yr−1 ) seen in the local Universe, yielding doubling times for their stellar mass of hundreds of Myr. The few star-forming Peas with Hubble Space Telescope imaging appear to have several clumps of bright star-forming regions and low surface density features that may indicate recent or ongoing mergers. The Peas are similar in size, mass, luminosity and metallicity to luminous blue compact galaxies. They are also similar to high-redshift ultraviolet-luminous galaxies, e.g. Lyman-break galaxies and Lyα emitters, and therefore provide a local laboratory with which to study the extreme star formation processes that occur in high-redshift galaxies. Studying starbursting galaxies as a function of redshift is essential to understanding the build up of stellar mass in the Universe.Peer reviewe
Limitations of Majority Agreement in Crowdsourced Image Interpretation
Crowdsourcing can efficiently complete tasks that are difficult to automate, but the quality of crowdsourced data is tricky to evaluate. Algorithms to grade volunteer work often assume that all tasks are similarly difficult, an assumption that is frequently false. We use a cropland identification game with over 2,600 participants and 165,000 unique tasks to investigate how best to evaluate the difficulty of crowdsourced tasks and to what extent this is possible based on volunteer responses alone. Inter-volunteer agreement exceeded 90% for about 80% of the images and was negatively correlated with volunteer-expressed uncertainty about image classification. A total of 343 relatively difficult images were independently classified as cropland, non-cropland or impossible by two experts. The experts disagreed weakly (one said impossible while the other rated as cropland or non-cropland) on 27% of the images, but disagreed strongly (cropland vs. non-cropland) on only 7%. Inter-volunteer disagreement increased significantly with inter-expert disagreement. While volunteers agreed with expert classifications for most images, over 20% would have been mis-categorized if only the volunteers’ majority vote was used. We end with a series of recommendations for managing the challenges posed by heterogeneous tasks in crowdsourcing campaigns
Galaxy Zoo: Motivations of Citizen Scientists
Citizen science, in which volunteers work with professional scientists to
conduct research, is expanding due to large online datasets. To plan projects,
it is important to understand volunteers' motivations for participating. This
paper analyzes results from an online survey of nearly 11,000 volunteers in
Galaxy Zoo, an astronomy citizen science project. Results show that volunteers'
primary motivation is a desire to contribute to scientific research. We
encourage other citizen science projects to study the motivations of their
volunteers, to see whether and how these results may be generalized to inform
the field of citizen science.Comment: 41 pages, including 6 figures and one appendix. In press at Astronomy
Education Revie
Supporting citizen inquiry: an investigation of Moon rock
Citizen inquiry is an innovative way for non-professionals to engage in practical scientific activities, in which they take the role of self-regulated scientists in informal learning contexts. This type of activity has similarities to inquiry-based learning and to citizen science, but also important differences. To understand the challenges of supporting citizen inquiry, a prototype system and activity has been developed: the Moon Rock Explorer. Based on the nQuire Toolkit, this offers people without geology expertise an open investigation into authentic specimens of Moon rock, using a Virtual Microscope. The Moon Rock Explorer inquiry has been evaluated in an informal learning context with PhD students from the Open University. Results of the evaluation raise issues related to motivation and interaction between inquiry participants. They also provide evidence that the integration of scientific tools was successful and that the nQuire Toolkit is suitable to deploy and enact citizen inquiries
Galaxy Zoo: Exploring the Motivations of Citizen Science Volunteers
The Galaxy Zoo citizen science website invites anyone with an Internet
connection to participate in research by classifying galaxies from the Sloan
Digital Sky Survey. As of April 2009, more than 200,000 volunteers had made
more than 100 million galaxy classifications. In this paper, we present results
of a pilot study into the motivations and demographics of Galaxy Zoo
volunteers, and define a technique to determine motivations from free responses
that can be used in larger multiple-choice surveys with similar populations.
Our categories form the basis for a future survey, with the goal of determining
the prevalence of each motivation.Comment: 15 pages, 3 figure
Galaxy Zoo: Reproducing Galaxy Morphologies Via Machine Learning
We present morphological classifications obtained using machine learning for
objects in SDSS DR6 that have been classified by Galaxy Zoo into three classes,
namely early types, spirals and point sources/artifacts. An artificial neural
network is trained on a subset of objects classified by the human eye and we
test whether the machine learning algorithm can reproduce the human
classifications for the rest of the sample. We find that the success of the
neural network in matching the human classifications depends crucially on the
set of input parameters chosen for the machine-learning algorithm. The colours
and parameters associated with profile-fitting are reasonable in separating the
objects into three classes. However, these results are considerably improved
when adding adaptive shape parameters as well as concentration and texture. The
adaptive moments, concentration and texture parameters alone cannot distinguish
between early type galaxies and the point sources/artifacts. Using a set of
twelve parameters, the neural network is able to reproduce the human
classifications to better than 90% for all three morphological classes. We find
that using a training set that is incomplete in magnitude does not degrade our
results given our particular choice of the input parameters to the network. We
conclude that it is promising to use machine- learning algorithms to perform
morphological classification for the next generation of wide-field imaging
surveys and that the Galaxy Zoo catalogue provides an invaluable training set
for such purposes.Comment: 13 Pages, 5 figures, 10 tables. Accepted for publication in MNRAS.
Revised to match accepted version
Galaxy Zoo: Dust in Spirals
We investigate the effect of dust on spiral galaxies by measuring the
inclination-dependence of optical colours for 24,276 well-resolved SDSS
galaxies visually classified in Galaxy Zoo. We find clear trends of reddening
with inclination which imply a total extinction from face-on to edge-on of 0.7,
0.6, 0.5 and 0.4 magnitudes for the ugri passbands. We split the sample into
"bulgy" (early-type) and "disky" (late-type) spirals using the SDSS fracdeV (or
f_DeV) parameter and show that the average face-on colour of "bulgy" spirals is
redder than the average edge-on colour of "disky" spirals. This shows that the
observed optical colour of a spiral galaxy is determined almost equally by the
spiral type (via the bulge-disk ratio and stellar populations), and reddening
due to dust. We find that both luminosity and spiral type affect the total
amount of extinction, with "disky" spirals at M_r ~ -21.5 mags having the most
reddening. This decrease of reddening for the most luminous spirals has not
been observed before and may be related to their lower levels of recent star
formation. We compare our results with the latest dust attenuation models of
Tuffs et al. We find that the model reproduces the observed trends reasonably
well but overpredicts the amount of u-band attenuation in edge-on galaxies. We
end by discussing the effects of dust on large galaxy surveys and emphasize
that these effects will become important as we push to higher precision
measurements of galaxy properties and their clustering.Comment: MNRAS in press. 25 pages, 22 figures (including an abstract comparing
GZ classifications with common automated methods for selecting disk/early
type galaxies in SDSS data). v2 corrects typos found in proof
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